import numpy as np
import cv2 as cv
import os
import time

yolo_dir = 'D:\GraduationProject\ObjectDection'  # YOLO文件路径
weightsPath = os.path.join(yolo_dir, 'yolov3.weights')  # 权重文件
configPath = os.path.join(yolo_dir, 'yolov3.cfg')  # 配置文件
labelsPath = os.path.join(yolo_dir, 'coco.names')  # label名称
imgPath = os.path.join(yolo_dir, 'test.jpg')  # 测试图像
CONFIDENCE = 0.5  # 过滤弱检测的最小概率
THRESHOLD = 0.4  # 非最大值抑制阈值

# 加载网络、配置权重
net = cv.dnn.readNetFromDarknet(configPath, weightsPath)  # #  利用下载的文件
print("[INFO] loading YOLO from disk...")  # # 可以打印下信息

def ObjectDect(img):
    blobImg = cv.dnn.blobFromImage(img, 1.0/255.0, (416, 416), None, True, False)   # # net需要的输入是blob格式的，用blobFromImage这个函数来转格式
    net.setInput(blobImg)  # # 调用setInput函数将图片送入输入层

    # 获取网络输出层信息（所有输出层的名字），设定并前向传播
    outInfo = net.getUnconnectedOutLayersNames()  # # 前面的yolov3架构也讲了，yolo在每个scale都有输出，outInfo是每个scale的名字信息，供net.forward使用
    start = time.time()
    layerOutputs = net.forward(outInfo)  # 得到各个输出层的、各个检测框等信息，是二维结构。
    end = time.time()
    print("[INFO] YOLO took {:.6f} seconds".format(end - start))  # # 可以打印下信息

    # 拿到图片尺寸
    (H, W) = img.shape[:2]
    # 过滤layerOutputs
    # layerOutputs的第1维的元素内容: [center_x, center_y, width, height, objectness, N-class score data]
    # 过滤后的结果放入：
    boxes = [] # 所有边界框（各层结果放一起）
    confidences = [] # 所有置信度
    classIDs = [] # 所有分类ID

    # # 1）过滤掉置信度低的框框
    for out in layerOutputs:  # 各个输出层
        for detection in out:  # 各个框框
            # 拿到置信度
            scores = detection[5:]  # 各个类别的置信度
            classID = np.argmax(scores)  # 最高置信度的id即为分类id
            confidence = scores[classID]  # 拿到置信度

            # 根据置信度筛查
            if confidence > CONFIDENCE:
                box = detection[0:4] * np.array([W, H, W, H])  # 将边界框放会图片尺寸
                (centerX, centerY, width, height) = box.astype("int")
                x = int(centerX - (width / 2))
                y = int(centerY - (height / 2))
                boxes.append([x, y, int(width), int(height)])
                confidences.append(float(confidence))
                classIDs.append(classID)

    # # 2）应用非最大值抑制(non-maxima suppression，nms)进一步筛掉
    idxs = cv.dnn.NMSBoxes(boxes, confidences, CONFIDENCE, THRESHOLD) # boxes中，保留的box的索引index存入idxs
    # 得到labels列表
    with open(labelsPath, 'rt') as f:
        labels = f.read().rstrip('\n').split('\n')
    # 应用检测结果
    np.random.seed(42)
    COLORS = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")  # 框框显示颜色，每一类有不同的颜色，每种颜色都是由RGB三个值组成的，所以size为(len(labels), 3)
    if len(idxs) > 0:
        for i in idxs.flatten():  # indxs是二维的，第0维是输出层，所以这里把它展平成1维
            (x, y) = (boxes[i][0], boxes[i][1])
            (w, h) = (boxes[i][2], boxes[i][3])

            color = [int(c) for c in COLORS[classIDs[i]]]
            cv.rectangle(img, (x, y), (x+w, y+h), color, 2)  # 线条粗细为2px
            text = "{}: {:.4f}".format(labels[classIDs[i]], confidences[i])
            cv.putText(img, text, (x, y-5), cv.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)  # cv.FONT_HERSHEY_SIMPLEX字体风格、0.5字体大小、粗细2px


    return img
    # cv.imshow('detected image', img)
    # cv.waitKey(0)


def TagOnPic(radarTarget, frame):
    targetnum = radarTarget['RadarTagnum']
    if targetnum == 0:
        return frame
    for item in radarTarget['RadarTaglist']:
        distance = item['distance'] * 1.0 / 100
        posX = int(item['X'])
        posY = int(item['Y'])
        cv.circle(frame, (posX, posY), 1, (0, 255, 0), 5)
        disstr = "distance = " + str(distance)
        cv.putText(frame, disstr, (posX - 50, posY - 50), cv.FONT_HERSHEY_PLAIN, 2, (0, 255, 0), 5)
    return frame
